Publication Date: 2023
Environmental Science and Pollution Research (09441344)30(12)pp. 33567-33586
The problem of traffic congestion and the environmental issues related to air pollution are among the essential problems of urban management that metropolitan cities are trying to mitigate. Given that the contribution of motor vehicles to air pollution is significant, both goals are achieved by managing urban transport. Among the various methods of travel demand management, congestion pricing is a very efficient measure. This study tried to simultaneously increase the efficiency of the transportation network and reduce the environmental effects by using a bi-level model for the multi-modal network. For this purpose, the upper-level model minimizes the objective function, i.e., pollution emission costs and overall commuting costs. The lower level also has a transportation network model that provides the condition of user equilibrium. The genetic and Frank-Wolfe algorithms have been used to solve the bi-level programming model. Two pricing schemes, cordon-based and link-based, are used to investigate and assist policymakers. The proposed algorithm is also applied to a real-world road network in Isfahan, Iran. The results of the proposed models for different pricing strategies were compared. According to the results, both pricing schemes mitigate traffic congestion and pollution, although the reduction in pollution outside the cordon is less than inside. Demand has also shifted from the private car mode to public transportation by an average of 15%. However, link-based pricing provides better performance than cordon-based pricing. This study indicated that a higher total collected toll in link-based pricing is accompanied by a sharper reduction in congestion and pollution mitigation, which can be spent on alternative facilities and infrastructure by the municipality, such as the development of public transportation and parking. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
